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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Steganalysis Using High-Dimensional Features Derived from Co-occurrence Matrix and Class-Wise Non-Principal Components Analysis (CNPCA)
verfasst von : Guorong Xuan, Yun Q. Shi, Cong Huang, Dongdong Fu, Xiuming Zhu, Peiqi Chai, Jianjiong Gao
Erschienen in: Digital Watermarking
Verlag: Springer Berlin Heidelberg
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This paper presents a novel steganalysis scheme with high-dimensional feature vectors derived from co-occurrence matrix in either spatial domain or JPEG coefficient domain, which is sensitive to data embedding process. The class-wise non-principal components analysis (CNPCA) is proposed to solve the problem of the classification in the high-dimensional feature vector space. The experimental results have demonstrated that the proposed scheme outperforms the existing steganalysis techniques in attacking the commonly used steganographic schemes applied to spatial domain (Spread-Spectrum, LSB, QIM) or JPEG domain (OutGuess, F5, Model-Based).